The mobile management system is a system that acquires the distribution in statuses typified by space coordinates such as current positions of the mobile units, and executes various kinds of statistical processing based thereupon. As the typical one, for example, frequency of the occurrence of crimes can be shown on a map in a unit of city, town, and village in various viewpoints such as “by each city, town, village”, “by each type of crimes”, etc., on the website of Non-Patent Document 1.
Further, as shown on the website of Non-Patent Document 2, it is possible to show traffic jam information of roads on a map based on positional information of each car running on the roads. In that case, it is possible to display the roads with different colors according to the extent of the jammed states. There are also marks for each region, types of the roads, and the like. This does not simply show the states of the current traffic jam on the roads but is also utilized for prediction of traffic jams and the like.
Further, depicted in Non-Patent Document 3 is a mobile management system which acquires current positions of each mobile unit by utilizing positional information acquired by each of the mobile units such as mobile phone terminals with GPS (Global Positioning System), Wi-Fi (Wireless Fidelty), and the like, and takes the positional information of a great number of mobile units as macro information. It is expected to be utilized for urban planning and the like through taking the positional information as the macro information and analyzing the behavior thereof. Depicted in Non-Patent Document 4 is K-th nearest neighbor clustering that is one of well-known methods regarding detection of distance distribution (will be described later in more details).
Depicted in Patent Document 1 is an information distribution service which predicts the distribution state of a mobile information terminal having specific user attribution. Depicted in Patent Document 2 is a technique which forms a micro machine in a specific layout pattern. Depicted in Patent Document 3 is a technique which acquires “presence information” that shows the status of the user of a mobile terminal from the communication status of the mobile terminal.
Depicted in Patent Document 4 is a behavior grasping device which grasps the behavior of a user based on the time of starting and terminating communication with an access point of a radio LAN. Depicted in Patent Document 5 is a radio communication system which selects a system to be used from a plurality of radio systems. Depicted in Patent Document 6 is a technique for classifying the attribute of consumers.
Patent Document 1: WO 2005/038680
Patent Document 2: Japanese Unexamined Patent Publication 2001-198989
Patent Document 3: Japanese Unexamined Patent Publication 2006-331200
Patent Document 4: Japanese Unexamined Patent Publication 2009-159336
Patent Document 5: Japanese Unexamined Patent Publication 2010-288009
Patent Document 6: Japanese Patent Application Publication 2010-5101947
Non-Patent Document 1: “Crime Information Map”, the Metropolitan Police Department, (Searched on Jan. 27, 2011),    Internet <URL: http://www.keishicho.metro.tokyo.jp/toukei/johomap/johomap.htm>    Non-Patent Document 2: “the Japan Road Traffic Information Center”, the Japan Road Traffic Information Center, (Searched on Jan. 27, 2011)    Internet <URL: http://www.jartic.or.jp/>    Non-Patent Document 3: “Mobile Spatial Statistics”, Mobile Society Research Institute (NTT Docomo), (Searched on Jan. 27, 2011),    Internet <URL: http:www.moba.ken.jp/research/research2010/r10_01>    Non-Patent Document 4: “Nearest-neighborclutter removal for estimating features in spatial point processes” Byers, S. D., and Raftery, A. E. 1998. Journal of American Statistical Association, 93 (442), pp. 577-584
The mobile management systems shown in Non-Patent Documents 1 to 3 generally requires a considerable amount of time for executing processing since a large scale of calculations are executed therein. For example, when it is desired to detect places where crimes, traffic jams, and the like are likely to occur, highly populated places, and the like, processing for dividing the target space into small sections and for creating histograms for each of the sections is required.
When the sectioning is done regularly such as in a tetragonal lattice form, the processing is simple. However, considering that the geographical positioning of the cities, towns, and villages as well as roads and the like greatly influence the result, simple sectioning such as the tetragonal lattice form may not be suitable to be used in some cases. It is because when border lines between the sections overlap on the part of high occurrence frequency, the occurrence frequency is divided roughly in half into two sections. As a result, the numerical value of the peak of the occurrence frequency is reduced to a half of that value.
Through detecting areas having a small distance between each of the nodes by finding the distance distribution of each of the terminals (nodes) without doing such sectioning, it is possible to identify the region where many nodes are aggregated without having the problems generated due to the sectioning. However, provided that the total number of the nodes is N, a calculation amount on the order of square of N is required for the processing. Therefore, it is not suited especially for a large-scaled mobile management system with which N is a huge number.
Further, it is also considered to employ a method which: assumes that the distance distribution of each of the nodes conform to a specific distribution model such as a Poisson point process; assumes that there is a difference in the generation rates of the points between a high density part and other parts; and detects only the high density part by comparing the fidelity to the distribution. Specifically, it is a method such as the K-th nearest neighbor clustering depicted in Non-Patent Document 4, which: assumes that the distribution of the distance to the node that is K-th closest conforms to Poisson distribution; and detects the part with a different generation rate by executing clustering. Other distribution models can also be utilized.
However, such method is also the processing that generally requires a large calculation amount. Thus, when the total number N of the nodes is increased, the calculation amount is increased exponentially. Therefore, such method is not suited for a large-scaled mobile management system, either. Especially, it is unsuitable for the cases where real-time display, operability, and the like are required (e.g., case of traffic jam information in Non-Patent Document 2, case of displaying behaviors of the user of a mobile phone terminal in a real-time manner as in Non-Patent Document 3).
Further, positional information of each node is used for specifying the behavior of the user of each node. Thus, in terms of protecting the privacy and protecting personal information, there may be cases where it is required to perform processing (processing for making it unidentifiable) on the information content so that node (user) cannot be identified therefrom. When this processing is performed on the server side, the calculation amount is increased further.
Techniques capable of overcoming such issues are not depicted in Patent Documents 1 to 6 described above, either. Patent Document 1 is a technique which predicts positional information of mobile units. However, it does not include a structure for making it efficient to perform the calculation. The techniques of Patent Documents 2 to 6 are not originally targeted for that, and any content for enabling it to be converted to be used for such object is not depicted therein.
An object of the present invention is to provide a mobile management system, a mobile management server, a mobile management method, and a mobile management program capable of reducing the calculation amount for the positional information and performing analysis of the positional information quickly and in a real-time manner.